Yayın: Automatic Detection of Covid-19 with Bidirectional LSTM Network Using Deep Features Extracted from Chest X-ray Images
| dc.contributor.author | Akyol, Kemal | |
| dc.contributor.author | Şen, Baha | |
| dc.date.accessioned | 2026-01-04T15:36:10Z | |
| dc.date.issued | 2021-07-27 | |
| dc.description.abstract | The main aim of this study is to present an effective and distinct deep learning-based model that shows a successful performance in Covid-19 disease which has adversely affects humanity since 2020.Here, the performances of two deep learning architectures on the deep features are compared for Covid-19 disease.<br>In this context, the main contribution of this study is as follows:a) The effectiveness of the Deep Neural Networks (DNN) and Bidirectional Long Short Term Memory (Bi-LSTM) deep learning models were compared comprehensively.b) The performances of the models within the framework of the 5-fold cross-validation technique were verified on the validation datasets.Finally, a highly efficient deep learning model was derived for detecting Covid-19 disease. | |
| dc.description.uri | https://doi.org/10.1007/s12539-021-00463-2 | |
| dc.description.uri | https://link.springer.com/content/pdf/10.1007/s12539-021-00463-2.pdf | |
| dc.description.uri | https://dx.doi.org/10.6084/m9.figshare.15162072 | |
| dc.description.uri | https://dx.doi.org/10.6084/m9.figshare.15162072.v1 | |
| dc.description.uri | https://pubmed.ncbi.nlm.nih.gov/34313974 | |
| dc.description.uri | http://dx.doi.org/10.1007/s12539-021-00463-2 | |
| dc.description.uri | https://dx.doi.org/10.1007/s12539-021-00463-2 | |
| dc.description.uri | https://avesis.aybu.edu.tr/publication/details/5ad3de4d-439f-4d5f-838f-116e44d56401/oai | |
| dc.identifier.doi | 10.1007/s12539-021-00463-2 | |
| dc.identifier.eissn | 1867-1462 | |
| dc.identifier.endpage | 100 | |
| dc.identifier.issn | 1913-2751 | |
| dc.identifier.openaire | doi_dedup___::1a423489aee4d782fc7ce526f290489d | |
| dc.identifier.orcid | 0000-0002-2272-5243 | |
| dc.identifier.orcid | 0000-0003-3577-2548 | |
| dc.identifier.pubmed | 34313974 | |
| dc.identifier.scopus | 2-s2.0-85111390930 | |
| dc.identifier.startpage | 89 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12597/38937 | |
| dc.identifier.volume | 14 | |
| dc.identifier.wos | 000679875500001 | |
| dc.language.iso | eng | |
| dc.publisher | Springer Science and Business Media LLC | |
| dc.relation.ispartof | Interdisciplinary Sciences: Computational Life Sciences | |
| dc.rights | OPEN | |
| dc.subject | Deep Learning | |
| dc.subject | SARS-CoV-2 | |
| dc.subject | X-Rays | |
| dc.subject | COVID-19 | |
| dc.subject | Humans | |
| dc.subject | Original Research Article | |
| dc.subject | Neural Networks, Computer | |
| dc.subject.sdg | 3. Good health | |
| dc.title | Automatic Detection of Covid-19 with Bidirectional LSTM Network Using Deep Features Extracted from Chest X-ray Images | |
| dc.type | Article | |
| dspace.entity.type | Publication | |
| local.import.source | OpenAire | |
| local.indexed.at | WOS | |
| local.indexed.at | Scopus | |
| local.indexed.at | PubMed |
